Decorrelation-Based Feature Discovery: GRAVIER-Whole
Jose Tamez
2023-10-03
- 1 Effect of UPSTM-Based Decorrelation
on Feature Discovery
- 1.0.1 Loading the libraries
- 1.1 Material and Methods
- 1.2 The Data
- 1.3 Generaring the report
- 1.4 The heatmap of the data
- 1.5 The decorrelation
- 1.6 The heatmap of the decorrelated data
- 1.7 The correlation matrix after decorrelation
- 1.8 U-MAP Visualization of features
- 1.9 Univariate Analysis
- 1.10 Comparing ILAA vs PCA vs EFA
- 1.11 Effect on CAR modeling
1 Effect of UPSTM-Based Decorrelation on Feature Discovery
1.0.1 Loading the libraries
library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)1.1 Material and Methods
1.2 The Data
#data('gravier', package = 'datamicroarray')
#table(gravier$y)
load("~/GitHub/datamicroarray/data/gravier.RData")
gravierset <- as.data.frame(cbind(class=1*(gravier$y=="poor"),gravier$x))
gravier <- NULL
1.2.0.1 Standarize the names for the reporting
studyName <- "GRAVIER"
dataframe <- gravierset
outcome <- "class"
TopVariables <- 10
thro <- 0.80
cexheat = 0.151.3 Generaring the report
1.3.1 Libraries
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")1.3.2 Data specs
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))| rows | col |
|---|---|
| 168 | 2905 |
pander::pander(table(dataframe[,outcome]))| 0 | 1 |
|---|---|
| 111 | 57 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500 1.3.3 Scaling the data
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData1.4 The heatmap of the data
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}1.4.0.1 Correlation Matrix of the Data
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}1.5 The decorrelation
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> g1int824 g4D02 g1int718 g10E08 g1int23 g5B03
#> g2E09 g7F07 g1A01 g3C09 g3H08 g1A08
#> 0.5669535 0.6771084 0.8096386 0.2306368 0.2034423 0.5913941
#>
#> Included: 2905 , Uni p: 5.163511e-05 , Base Size: 14 , Rcrit: 0.2950683
#>
#>
1 <R=0.983,thr=0.950>, Top: 6< 5 >[Fa= 6 ]( 6 , 14 , 0 ),<|><>Tot Used: 20 , Added: 14 , Zero Std: 0 , Max Cor: 0.952
#>
2 <R=0.952,thr=0.950>, Top: 1< 1 >[Fa= 7 ]( 1 , 1 , 6 ),<|><>Tot Used: 22 , Added: 1 , Zero Std: 0 , Max Cor: 0.950
#>
3 <R=0.950,thr=0.900>, Top: 51< 3 >[Fa= 54 ]( 51 , 82 , 7 ),<|><>Tot Used: 149 , Added: 82 , Zero Std: 0 , Max Cor: 0.914
#>
4 <R=0.914,thr=0.900>, Top: 3< 1 >[Fa= 57 ]( 3 , 3 , 54 ),<|><>Tot Used: 155 , Added: 3 , Zero Std: 0 , Max Cor: 0.900
#>
5 <R=0.900,thr=0.800>, Top: 204< 9 >..[Fa= 232 ]( 203 , 460 , 57 ),<|><>Tot Used: 777 , Added: 460 , Zero Std: 0 , Max Cor: 0.897
#>
6 <R=0.897,thr=0.800>, Top: 42< 13 >[Fa= 271 ]( 42 , 74 , 232 ),<|><>Tot Used: 887 , Added: 74 , Zero Std: 0 , Max Cor: 0.864
#>
7 <R=0.864,thr=0.800>, Top: 5< 3 >[Fa= 276 ]( 5 , 9 , 271 ),<|><>Tot Used: 901 , Added: 9 , Zero Std: 0 , Max Cor: 0.800
#>
8 <R=0.800,thr=0.800>
#>
[ 8 ], 0.7999747 Decor Dimension: 901 Nused: 901 . Cor to Base: 618 , ABase: 2905 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))61.1
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))46.4
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))3
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))2.85
varratio <- attr(DEdataframe,"VarRatio")
pander::pander(tail(varratio))| La_g4B02 | La_g1int832 | La_g1int829 | La_g6A10 | La_g1int823 | La_g1int828 |
|---|---|---|---|---|---|
| 0.0602 | 0.0568 | 0.0551 | 0.0548 | 0.0401 | 0.0344 |
1.5.1 The decorrelation matrix
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}1.5.2 Formulas Network
Displaying the features associations
par(op)
#if ((ncol(dataframe) < 1000) && (ncol(dataframe) > 10))
#{
# DEdataframeB <- ILAA(dataframe,verbose=TRUE,thr=thro,bootstrap=30)
transform <- attr(DEdataframe,"UPLTM") != 0
tnames <- colnames(transform)
colnames(transform) <- str_remove_all(colnames(transform),"La_")
transform <- abs(transform*cor(dataframe[,rownames(transform)])) # The weights are proportional to the observed correlation
VertexSize <- attr(DEdataframe,"fscore") # The size depends on the variable independence relevance (fscore)
names(VertexSize) <- str_remove_all(names(VertexSize),"La_")
VertexSize <- 10*(VertexSize-min(VertexSize))/(max(VertexSize)-min(VertexSize)) # Normalization
VertexSize <- VertexSize[rownames(transform)]
rsum <- apply(1*(transform !=0),1,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
csum <- apply(1*(transform !=0),2,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
ntop <- min(10,length(rsum))
topfeatures <- unique(c(names(rsum[order(-rsum)])[1:ntop],names(csum[order(-csum)])[1:ntop]))
rtrans <- transform[topfeatures,]
csum <- (apply(1*(rtrans !=0),2,sum) > 1)
rtrans <- rtrans[,csum]
topfeatures <- unique(c(topfeatures,colnames(rtrans)))
print(ncol(transform))
#> [1] 901
transform <- transform[topfeatures,topfeatures]
print(ncol(transform))
#> [1] 20
if (ncol(transform)>100)
{
csum <- (apply(1*(transform !=0),2,sum) > 1) & (apply(1*(transform !=0),1,sum) > 1)
transform <- transform[csum,csum]
print(ncol(transform))
}
if (ncol(transform) < 150)
{
gplots::heatmap.2(transform,
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Red Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
VertexSize <- VertexSize[colnames(transform)]
gr <- graph_from_adjacency_matrix(transform,mode = "directed",diag = FALSE,weighted=TRUE)
gr$layout <- layout_with_fr
fc <- cluster_optimal(gr)
plot(fc, gr,
edge.width = 2*E(gr)$weight,
vertex.size=VertexSize,
edge.arrow.size=0.5,
edge.arrow.width=0.5,
vertex.label.cex=(0.15+0.05*VertexSize),
vertex.label.dist=0.5 + 0.05*VertexSize,
main="Top Feature Association")
}
par(op)1.7 The correlation matrix after decorrelation
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after ILAA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
1.8 U-MAP Visualization of features
1.8.1 The UMAP on Raw Data
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
topvars <- univariate_BinEnsemble(dataframe,outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),dataframe,family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))g1CNS507, g1CNS382, g1CNS91, g1CNS105, g1CNS26 and g1int804
# names(topvars)
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(dataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(dataframe[1:numsub,varlist],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
#}1.8.2 The decorralted UMAP
varlistcV <- names(varratio[varratio >= 0.025])
topvars <- univariate_BinEnsemble(DEdataframe[,varlistcV],outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),DEdataframe,family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))g1CNS507, g1int340, g1int812, g9E01, g8D02 and g1CNS29
varlistcV <- varlistcV[varlistcV != outcome]
# DEdataframe[,outcome] <- as.numeric(DEdataframe[,outcome])
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(DEdataframe[1:numsub,varlistcV],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After ILAA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
#}1.9 Univariate Analysis
1.9.1 Univariate
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")100 : g1A07 200 : g3H03 300 : g1E07 400 : g4F03 500 : g4C05
600 : g1int292 700 : g1int356 800 : g7A03 900 : g4D11 1000 :
g1int577
1100 : g1CNS420 1200 : g7G07 1300 : g1int785 1400 : g1CNS59 1500 :
g1CNS178
1600 : g1int949 1700 : g1int1028 1800 : g1int1089 1900 : g11D05 2000 :
g1int1222
2100 : g1int1298 2200 : g1int1376 2300 : g1int1449 2400 : g10E08 2500 :
g1CNS90
2600 : g7F11 2700 : g1int1693 2800 : g1CNS93 2900 : g1int1800
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)100 : g1A07 200 : g3H03 300 : La_g1E07 400 : g4F03 500 : g4C05
600 : La_g1int292 700 : g1int356 800 : g7A03 900 : g4D11 1000 :
g1int577
1100 : g1CNS420 1200 : La_g7G07 1300 : g1int785 1400 : La_g1CNS59 1500 :
g1CNS178
1600 : g1int949 1700 : La_g1int1028 1800 : La_g1int1089 1900 : g11D05
2000 : g1int1222
2100 : g1int1298 2200 : g1int1376 2300 : La_g1int1449 2400 : g10E08 2500
: g1CNS90
2600 : g7F11 2700 : g1int1693 2800 : La_g1CNS93 2900 : g1int1800
1.9.2 Final Table
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| g1CNS507 | -0.250 | 0.222 | -0.0248 | 0.195 | 0.075349 | 0.796 |
| g1CNS105 | 0.254 | 0.270 | 0.0533 | 0.172 | 0.009926 | 0.749 |
| g1CNS382 | -0.171 | 0.173 | -0.0354 | 0.145 | 0.045504 | 0.745 |
| g1int804 | -0.250 | 0.220 | -0.0775 | 0.205 | 0.020286 | 0.743 |
| g1CNS91 | 0.310 | 0.294 | 0.0994 | 0.138 | 0.003638 | 0.742 |
| g1CNS26 | -0.145 | 0.169 | -0.0101 | 0.125 | 0.657242 | 0.738 |
| g1int340 | -0.157 | 0.183 | -0.0169 | 0.139 | 0.028840 | 0.737 |
| g1CNS70 | 0.152 | 0.177 | 0.0352 | 0.115 | 0.022558 | 0.731 |
| g1CNS158 | 0.260 | 0.251 | 0.1007 | 0.167 | 0.000401 | 0.731 |
| g1CNS28 | 0.218 | 0.218 | 0.0744 | 0.143 | 0.001961 | 0.726 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| g1CNS507 | -0.249546 | 0.2224 | -0.024786 | 0.1946 | 0.075349 | 0.796 |
| g1int340 | -0.157144 | 0.1829 | -0.016911 | 0.1391 | 0.028840 | 0.737 |
| g8D02 | -0.086458 | 0.1772 | 0.027399 | 0.1370 | 0.019618 | 0.725 |
| g1CNS159 | 0.192334 | 0.2488 | 0.046005 | 0.1735 | 0.000826 | 0.723 |
| g1int1671 | 0.126334 | 0.1404 | 0.039686 | 0.1218 | 0.099178 | 0.719 |
| g9E01 | 0.091105 | 0.1633 | -0.004645 | 0.0984 | 0.039398 | 0.717 |
| g1int812 | -0.192608 | 0.2140 | -0.046220 | 0.1755 | 0.235071 | 0.716 |
| g1CNS74 | 0.124918 | 0.1377 | 0.039057 | 0.1157 | 0.163757 | 0.716 |
| g8F04 | 0.022967 | 0.0898 | -0.046750 | 0.0893 | 0.805754 | 0.710 |
| g5G03 | -0.113922 | 0.1569 | -0.014816 | 0.1450 | 0.040736 | 0.706 |
| La_g1int829 | 0.027457 | 0.0662 | -0.018160 | 0.0654 | 0.686753 | 0.700 |
| La_g4F01 | 0.038348 | 0.0752 | -0.000825 | 0.0780 | 0.311967 | 0.677 |
| La_g1int1102 | -0.075044 | 0.0969 | -0.020829 | 0.0888 | 0.626136 | 0.674 |
| La_g6H03 | 0.000405 | 0.1480 | -0.045865 | 0.0647 | 0.370420 | 0.667 |
| La_g1CNS91 | 0.081214 | 0.1814 | 0.012656 | 0.0845 | 0.130390 | 0.667 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))| mean | total | fraction |
|---|---|---|
| 2 | 643 | 0.221 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| g1CNS507 | NA | -0.249546 | 0.2224 | -0.024786 | 0.1946 | 0.075349 | 0.796 | 0.796 | 0 |
| g1CNS5071 | NA | -0.249546 | 0.2224 | -0.024786 | 0.1946 | 0.075349 | 0.796 | NA | NA |
| g1CNS105 | NA | 0.254206 | 0.2698 | 0.053317 | 0.1720 | 0.009926 | 0.749 | 0.749 | NA |
| g1CNS382 | NA | -0.171019 | 0.1731 | -0.035377 | 0.1449 | 0.045504 | 0.745 | 0.745 | NA |
| g1int804 | NA | -0.249725 | 0.2195 | -0.077451 | 0.2051 | 0.020286 | 0.743 | 0.743 | NA |
| g1CNS91 | NA | 0.309879 | 0.2941 | 0.099384 | 0.1383 | 0.003638 | 0.742 | 0.742 | NA |
| g1CNS26 | NA | -0.144676 | 0.1686 | -0.010129 | 0.1253 | 0.657242 | 0.738 | 0.738 | NA |
| g1int340 | NA | -0.157144 | 0.1829 | -0.016911 | 0.1391 | 0.028840 | 0.737 | 0.737 | 0 |
| g1int3401 | NA | -0.157144 | 0.1829 | -0.016911 | 0.1391 | 0.028840 | 0.737 | NA | NA |
| g1CNS70 | NA | 0.152301 | 0.1769 | 0.035188 | 0.1152 | 0.022558 | 0.731 | 0.731 | NA |
| g1CNS158 | NA | 0.259813 | 0.2505 | 0.100672 | 0.1674 | 0.000401 | 0.731 | 0.731 | NA |
| g1CNS28 | NA | 0.218429 | 0.2175 | 0.074444 | 0.1432 | 0.001961 | 0.726 | 0.726 | NA |
| g8D02 | NA | -0.086458 | 0.1772 | 0.027399 | 0.1370 | 0.019618 | 0.725 | 0.725 | 0 |
| g1CNS159 | NA | 0.192334 | 0.2488 | 0.046005 | 0.1735 | 0.000826 | 0.723 | 0.723 | 11 |
| g1int1671 | NA | 0.126334 | 0.1404 | 0.039686 | 0.1218 | 0.099178 | 0.719 | 0.719 | 0 |
| g9E01 | NA | 0.091105 | 0.1633 | -0.004645 | 0.0984 | 0.039398 | 0.717 | 0.717 | 0 |
| g1int812 | NA | -0.192608 | 0.2140 | -0.046220 | 0.1755 | 0.235071 | 0.716 | 0.716 | 12 |
| g1CNS74 | NA | 0.124918 | 0.1377 | 0.039057 | 0.1157 | 0.163757 | 0.716 | 0.716 | 0 |
| g8F04 | NA | 0.022967 | 0.0898 | -0.046750 | 0.0893 | 0.805754 | 0.710 | 0.710 | 0 |
| g5G03 | NA | -0.113922 | 0.1569 | -0.014816 | 0.1450 | 0.040736 | 0.706 | 0.706 | 0 |
| La_g1int829 | - (1.024)g1int834 + g1int829 | 0.027457 | 0.0662 | -0.018160 | 0.0654 | 0.686753 | 0.700 | 0.617 | -1 |
| La_g4F01 | + g4F01 - (0.613)g1int98 | 0.038348 | 0.0752 | -0.000825 | 0.0780 | 0.311967 | 0.677 | 0.617 | -1 |
| La_g1int1102 | - (0.868)g1int1101 + g1int1102 | -0.075044 | 0.0969 | -0.020829 | 0.0888 | 0.626136 | 0.674 | 0.644 | -1 |
| La_g6H03 | + g6H03 - (1.057)g1int1515 | 0.000405 | 0.1480 | -0.045865 | 0.0647 | 0.370420 | 0.667 | 0.523 | -1 |
| La_g1CNS91 | - (0.985)g5F04 + g1CNS91 | 0.081214 | 0.1814 | 0.012656 | 0.0845 | 0.130390 | 0.667 | 0.742 | 0 |
1.10 Comparing ILAA vs PCA vs EFA
1.10.1 PCA
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
1.10.2 EFA
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}1.11 Effect on CAR modeling
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))| 0 | 1 | |
|---|---|---|
| 0 | 107 | 4 |
| 1 | 10 | 47 |
pander::pander(ptab$detail[c(5,3,4,6),])| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.917 | 0.864 | 0.954 |
| 3 | se | 0.825 | 0.701 | 0.913 |
| 4 | sp | 0.964 | 0.910 | 0.990 |
| 6 | diag.or | 125.725 | 37.521 | 421.276 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe[,c(outcome,varlistcV)],control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="ILAA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))| 0 | 1 | |
|---|---|---|
| 0 | 103 | 8 |
| 1 | 6 | 51 |
pander::pander(ptab$detail[c(5,3,4,6),])| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.917 | 0.864 | 0.954 |
| 3 | se | 0.895 | 0.785 | 0.960 |
| 4 | sp | 0.928 | 0.863 | 0.968 |
| 6 | diag.or | 109.438 | 36.051 | 332.214 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))| 0 | 1 | |
|---|---|---|
| 0 | 105 | 6 |
| 1 | 27 | 30 |
pander::pander(ptab$detail[c(5,3,4,6),])| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.804 | 0.735 | 0.861 |
| 3 | se | 0.526 | 0.390 | 0.660 |
| 4 | sp | 0.946 | 0.886 | 0.980 |
| 6 | diag.or | 19.444 | 7.347 | 51.459 |
par(op)1.11.1 EFA
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))| 0 | 1 | |
|---|---|---|
| 0 | 107 | 4 |
| 1 | 10 | 47 |
pander::pander(ptab$detail[c(5,3,4,6),])| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.917 | 0.864 | 0.954 |
| 3 | se | 0.825 | 0.701 | 0.913 |
| 4 | sp | 0.964 | 0.910 | 0.990 |
| 6 | diag.or | 125.725 | 37.521 | 421.276 |
par(op)